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Robust BCa-JaB method as a diagnostic tool for linear regression models

Beyaztas, Ufuk; Alin, Aylin; Martin, Michael


The Jackknife-after-bootstrap (JaB) technique originally developed by Efron [8] has been proposed as an approach to improve the detection of influential observations in linear regression models by Martin and Roberts [12] and Beyaztas and Alin [2]. The method is based on the use of percentile-method confidence intervals to provide improved cut-off values for several single case-deletion influence measures. In order to improve JaB, we propose using robust versions of Efron [7]'s bias-corrected...[Show more]

CollectionsANU Research Publications
Date published: 2014
Type: Journal article
Source: Journal of Applied Statistics
DOI: 10.1080/02664763.2014.881788


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